Research Associate with a doctorate (m/f/d) in the field of physics, chemistry, materials science or a comparable field

• Active involvement in the Focus Area Materials and the Materials Design Activity field, with a special focus on Materials Acceleration Platforms (MAPs)
• Contribution to building a team for simulation- and AI-supported control of autonomous laboratory processes
• Development of methods for identifying suitable descriptors for pre-screening materials within the framework of Materials Acceleration Platforms
• Research on models and algorithms for evaluating and predicting the synthesizability and synthesis recipes of materials
• Research on the integration of generative models into the control of laboratory processes
• Development and application of active learning methods to efficiently narrow the material search space
• Establishment and implementation of multifidelity approaches that optimally link simulation and experimental data
• Design and development of data-driven workflows for Materials Acceleration Platforms
• Collaboration with internal and external partners on the topics of MAPs, material simulation, and digital material design
• Supporting the department in acquiring and coordinating research and development projects on material design (with a focus on Materials Acceleration Platforms)
• Supporting the department in establishing a research data management system
• Supervision of doctoral candidates, students, and scientific staff (in third-party funded projects)
• Independently securing third-party funding

• Very good academic degree (Diploma/Master) in physics, chemistry, materials science, or a comparable field
• At least a very good PhD in physics, chemistry, materials science, or a related discipline
• Extensive knowledge in solid-state chemistry and physics (e.g. demonstrated through completed modules and publication experience)
• Excellent programming skills (especially in Python) (e.g. demonstrated through published software contributions)
• Comprehensive knowledge of quantum mechanics (e.g. demonstrated through the focus of studies or PhD)
• Comprehensive knowledge of density functional theory (e.g. demonstrated through the focus of studies or PhD)
• Several years of experience with density functional theory software (e.g., Vienna Ab Initio Simulation Package; demonstrated through publications)
• Several years of experience with machine learning in at least one of the areas of “generative models,” “active learning,” or “explainable AI” in the context of chemical research (e.g. demonstrated by publications and software contributions)
• Experience in the automation of quantum chemical calculations (software packages such as ase, pymatgen, atomate2) is required (e.g. demonstrated by publications or software contributions)
• Experience in securing external funding for projects 
• Extensive publication experience (more than 4 publications as first or last author)
• Research experience in the simulation of nanoparticles or metal-organic frameworks (MOFs) or covalent organic frameworks (COFs) is an advantage
• Very good command of English, both written and spoken
• German language skills are an advantage
• Good communication and information behaviour, goal-oriented and structured way of working, ability to work in a team and willingness to cooperate, willingness to learn

We welcome applications via the online application form by 23.06.2026. Alternatively, you can also send your application by post, quoting the Job-ID to:

Bundesanstalt für Materialforschung und -prüfung
Referat Z.3 - Personal
Unter den Eichen 87
12205 Berlin
GERMANY
www.bam.de

BAM promotes professional equality between women and men. We therefore particularly welcome applications from women. At the same time, we strive to reflect social diversity. Every application is therefore welcome, regardless of gender, cultural or social background, religion, ideology or sexual identity.

In addition, BAM has set itself the goal of promoting the professional participation of people with severe disabilities. The fulfillment of the job requirements is considered on an individual basis. Severely disabled persons or persons of equal status will be given preferential consideration if they are equally qualified.

The advertised position requires a high or low level of physical aptitude.